Signal processing systems often must measure several parameters of an incoming RF signal. For example, frequency, amplitude, pulse width and time of arrival. However, the signal of interest is often distorted by background noise and other signals. In digital signal processing systems, it has therefore become common to process signals by multiplying them with a weighting function which performs a filtering action on the signal of interest. The weighted signal stands out from the background more clearly so the parameters can then be measured with greater accuracy.
Typically, the weighting function is defined prior to being implemented in a signal processing system and the weighting function coefficients remain constant throughout the use of the system. This method of signal weighting requires only a multiplier and a stored set of weighting coefficients.
In digital signal processing, the sample window is always of finite length. This results in discontinuities at the ends of the sample window with respect to the signal. As a result, sidelobes are generated in the frequency domain. These sidelobes can obscure other signals of smaller amplitude occurring in the window at the same time, as shown in FIG. 1. This phenomenon also occurs in analog signal processing due to the finite bandwidth constraints of analog filters. FIG. 1 is a graphic illustration of a large signal pulse 20 which has been weighted using the constant weighting function system. The signal is plotted in both the time and frequency domain with respect to amplitude. The signal has substantial sidelobes 22 which almost completely obscure the small signal 24.
A more advanced method of processing which has been 20 used in the past to improve the signal to noise ratio (SNR) of a single signal is illustrated in FIGS. 2A-2G. FIG. 2A is a graphical representation of a conventional weighting function 5 as a function of amplitude and time applied to the time interval or window from time 1 to time 2. This time interval is the window of time used for sampling signal parameters. The vertical lines at times 4 and 8 represent the leading and trailing edge of a later occurring incoming signal pulse having an envelope 6.
The more advanced signal weighting commonly requires an edge detector, a weighting function generator, and a multiplier. The edge detector analyzes the incoming signal pulses. When there is no edge, a standard weighting function 5 is applied to the window 6 (FIG. 2A). FIG. 2B represents a later time when the sampling window from time 1 to time 2 has moved in time toward the pulse and partially overlaps the pulse. The weighting function is still applied to the whole window.
In FIG. 2C, the sample window overlaps the leading edge of the pulse in time for a significant portion of the sampling window. When the edge detector detects the leading edge 4 of a signal pulse and determines that the sample window 5 occupies a substantial part of the pulse envelope 6, the weighting function is only applied to the pulse envelope as illustrated by FIG. 2C to eliminate the inclusion of noise occurring before the pulse envelope. The weighting function is adapted to fit the signal envelope until the sample window is completely contained within the pulse envelope (FIG. 2D). The signal is then weighted with the constant weighting function matched to the sample window until the sample window overlaps the trailing edge of the pulse in time.
When the edge detector detects the trailing edge 8 of a signal pulse and determines that the sample window 5 overlaps the pulse envelope, the weighting function is only applied to the pulse envelope as illustrated by FIG. 25 to eliminate the inclusion of noise occurring after the pulse envelope. This continues until the sample window is no longer overlapping a substantial part of the pulse envelope 6 after which time, the entire sample window is weighted until the next signal is detected (FIGS. 2F and 2G).
This more advanced method of signal processing increases the signal to noise ratio, however, it does not significantly enhance reception of neighboring small signals. The present invention increases the signal to noise ratio and enhances reception of neighboring small signals. It also significantly reduces the sidelobes thereby reducing interference from neighboring large signals.